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<p>Sustainable development requires a balanced approach to economic growth, social progress, and environmental protection. While national-level assessments of Sustainable Development Goals (SDGs) are common, limited research has focused on clustering sub-national regions based on their SDG performance and examining the relational dynamics among them. Addressing this gap, the present study applies Fuzzy C-Means clustering to categorize Indian states into similar groups based on their SDG indicators. To enrich these findings, a network-based analysis is employed to explore interlinkages within clusters, revealing how states relate to one another in terms of SDG progress. The clustering identifies four distinct groups: Cluster-2, the most developed, excels in sustainability, human development, and economic indicators; Cluster-3 performs well but shows moderate lags in some areas; Clusters 1 and 4 consist of states with lower industrialization and socio-economic challenges, indicating the need for focused policy support. Transition path analysis shows faster improvements in Clusters 2 and 3, while Cluster-4 lags. Network analysis further highlights central and peripheral states within clusters, emphasizing intra-cluster diversity and the potential for knowledge sharing. These integrated insights stress the importance of tailored, state-specific strategies, inter-state collaboration, and governance reforms to accelerate SDG achievement.</p>

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Clustering Indian states on sustainable development goals: a fuzzy C-means and network-based analytical approach

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Soumyaranjan Jena,
  • Sayel Basel

摘要

Sustainable development requires a balanced approach to economic growth, social progress, and environmental protection. While national-level assessments of Sustainable Development Goals (SDGs) are common, limited research has focused on clustering sub-national regions based on their SDG performance and examining the relational dynamics among them. Addressing this gap, the present study applies Fuzzy C-Means clustering to categorize Indian states into similar groups based on their SDG indicators. To enrich these findings, a network-based analysis is employed to explore interlinkages within clusters, revealing how states relate to one another in terms of SDG progress. The clustering identifies four distinct groups: Cluster-2, the most developed, excels in sustainability, human development, and economic indicators; Cluster-3 performs well but shows moderate lags in some areas; Clusters 1 and 4 consist of states with lower industrialization and socio-economic challenges, indicating the need for focused policy support. Transition path analysis shows faster improvements in Clusters 2 and 3, while Cluster-4 lags. Network analysis further highlights central and peripheral states within clusters, emphasizing intra-cluster diversity and the potential for knowledge sharing. These integrated insights stress the importance of tailored, state-specific strategies, inter-state collaboration, and governance reforms to accelerate SDG achievement.